ml-finance-python

naive_bayes.py

(3363B)

 from __future__ import division, print_function
 import numpy as np
 import math
 from mlfromscratch.utils import train_test_split, normalize
 from mlfromscratch.utils import Plot, accuracy_score
 
 class NaiveBayes():
     """The Gaussian Naive Bayes classifier. """
     def fit(self, X, y):
         self.X, self.y = X, y
         self.classes = np.unique(y)
         self.parameters = []
         # Calculate the mean and variance of each feature for each class
         for i, c in enumerate(self.classes):
             # Only select the rows where the label equals the given class
             X_where_c = X[np.where(y == c)]
             self.parameters.append([])
             # Add the mean and variance for each feature (column)
             for col in X_where_c.T:
                 parameters = {"mean": col.mean(), "var": col.var()}
                 self.parameters[i].append(parameters)
 
     def _calculate_likelihood(self, mean, var, x):
         """ Gaussian likelihood of the data x given mean and var """
         eps = 1e-4 # Added in denominator to prevent division by zero
         coeff = 1.0 / math.sqrt(2.0 * math.pi * var + eps)
         exponent = math.exp(-(math.pow(x - mean, 2) / (2 * var + eps)))
         return coeff * exponent
 
     def _calculate_prior(self, c):
         """ Calculate the prior of class c
         (samples where class == c / total number of samples)"""
         frequency = np.mean(self.y == c)
         return frequency
 
     def _classify(self, sample):
         """ Classification using Bayes Rule P(Y|X) = P(X|Y)*P(Y)/P(X),
             or Posterior = Likelihood * Prior / Scaling Factor
 
         P(Y|X) - The posterior is the probability that sample x is of class y given the
                  feature values of x being distributed according to distribution of y and the prior.
         P(X|Y) - Likelihood of data X given class distribution Y.
                  Gaussian distribution (given by _calculate_likelihood)
         P(Y)   - Prior (given by _calculate_prior)
         P(X)   - Scales the posterior to make it a proper probability distribution.
                  This term is ignored in this implementation since it doesn't affect
                  which class distribution the sample is most likely to belong to.
 
         Classifies the sample as the class that results in the largest P(Y|X) (posterior)
         """
         posteriors = []
         # Go through list of classes
         for i, c in enumerate(self.classes):
             # Initialize posterior as prior
             posterior = self._calculate_prior(c)
             # Naive assumption (independence):
             # P(x1,x2,x3|Y) = P(x1|Y)*P(x2|Y)*P(x3|Y)
             # Posterior is product of prior and likelihoods (ignoring scaling factor)
             for feature_value, params in zip(sample, self.parameters[i]):
                 # Likelihood of feature value given distribution of feature values given y
                 likelihood = self._calculate_likelihood(params["mean"], params["var"], feature_value)
                 posterior *= likelihood
             posteriors.append(posterior)
         # Return the class with the largest posterior probability
         return self.classes[np.argmax(posteriors)]
 
     def predict(self, X):
         """ Predict the class labels of the samples in X """
         y_pred = [self._classify(sample) for sample in X]
         return y_pred